Extracting geographic knowledge from sensor intervention data using spatial association rules

Large networks of sensors are used to detect intrusions and provide security at the borders of the United States. Sensor signals are used to detect possible intrusions such as illegal immigration traffic in drugs, weapons, and smuggled goods at specific targeted geographic locations. GIS systems can be used to capture, store and analyze this location based intervention data. Using a GIS system, a spatial database can be generated from the sensor intervention data which can take into account relevant geographic information in the vicinity of the sensed interventions. Important geographic features that are close to the intervention locations such as: plateaus, hills, valleys or roadways can be extracted and added to the analysis using ArcGIS. GIS techniques alone cannot reveal meaningful hidden information within geographic data. We have developed an integrated approach involving data mining and GIS techniques to extract patterns and trends in geographic data that can aid and inform analysis. Our approach uses both spatial and association data mining techniques. Spatial data mining is the process of discovering previously unknown, interesting and potentially useful patterns from spatial datasets. Applying association rule mining to the spatial data can reveal additional important spatial relationships and help determine the relevance and importance of the sensor data. Spatial association rule mining was used to discover patterns in the intervention data, such as linking a sensed intrusion with a potentially hidden location such as a canyon, to infer a high probability of illegal traffic or immigration.